Revert "[SPARK-11689][ML] Add user guide and example code for LDA under spark.ml"

This reverts commit e359d5dcf5bd300213054ebeae9fe75c4f7eb9e7.

2 files changed, 0 insertions, 171 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java
deleted file mode 100644
index b3a7d2eb29..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLDAExample.java
+++ /dev/null
@@ -1,94 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.ml;
-
-import java.util.regex.Pattern;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.ml.clustering.LDA;
-import org.apache.spark.ml.clustering.LDAModel;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.sql.DataFrame;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.catalyst.expressions.GenericRow;
-import org.apache.spark.sql.types.Metadata;
-import org.apache.spark.sql.types.StructField;
-import org.apache.spark.sql.types.StructType;
-
-/**
- * An example demonstrating LDA
- * Run with
- * <pre>
- * bin/run-example ml.JavaLDAExample
- * </pre>
- */
-public class JavaLDAExample {
-
-  private static class ParseVector implements Function<String, Row> {
-    private static final Pattern separator = Pattern.compile(" ");
-
-    @Override
-    public Row call(String line) {
-      String[] tok = separator.split(line);
-      double[] point = new double[tok.length];
-      for (int i = 0; i < tok.length; ++i) {
-        point[i] = Double.parseDouble(tok[i]);
-      }
-      Vector[] points = {Vectors.dense(point)};
-      return new GenericRow(points);
-    }
-  }
-
-  public static void main(String[] args) {
-
-    String inputFile = "data/mllib/sample_lda_data.txt";
-
-    // Parses the arguments
-    SparkConf conf = new SparkConf().setAppName("JavaLDAExample");
-    JavaSparkContext jsc = new JavaSparkContext(conf);
-    SQLContext sqlContext = new SQLContext(jsc);
-
-    // Loads data
-    JavaRDD<Row> points = jsc.textFile(inputFile).map(new ParseVector());
-    StructField[] fields = {new StructField("features", new VectorUDT(), false, Metadata.empty())};
-    StructType schema = new StructType(fields);
-    DataFrame dataset = sqlContext.createDataFrame(points, schema);
-
-    // Trains a LDA model
-    LDA lda = new LDA()
-      .setK(10)
-      .setMaxIter(10);
-    LDAModel model = lda.fit(dataset);
-
-    System.out.println(model.logLikelihood(dataset));
-    System.out.println(model.logPerplexity(dataset));
-
-    // Shows the result
-    DataFrame topics = model.describeTopics(3);
-    topics.show(false);
-    model.transform(dataset).show(false);
-
-    jsc.stop();
-  }
-}
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala
deleted file mode 100644
index 419ce3d87a..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LDAExample.scala
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.ml
-
-// scalastyle:off println
-import org.apache.spark.{SparkContext, SparkConf}
-import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
-// $example on$
-import org.apache.spark.ml.clustering.LDA
-import org.apache.spark.sql.{Row, SQLContext}
-import org.apache.spark.sql.types.{StructField, StructType}
-// $example off$
-
-/**
- * An example demonstrating a LDA of ML pipeline.
- * Run with
- * {{{
- * bin/run-example ml.LDAExample
- * }}}
- */
-object LDAExample {
-
-  final val FEATURES_COL = "features"
-
-  def main(args: Array[String]): Unit = {
-
-    val input = "data/mllib/sample_lda_data.txt"
-    // Creates a Spark context and a SQL context
-    val conf = new SparkConf().setAppName(s"${this.getClass.getSimpleName}")
-    val sc = new SparkContext(conf)
-    val sqlContext = new SQLContext(sc)
-
-    // $example on$
-    // Loads data
-    val rowRDD = sc.textFile(input).filter(_.nonEmpty)
-      .map(_.split(" ").map(_.toDouble)).map(Vectors.dense).map(Row(_))
-    val schema = StructType(Array(StructField(FEATURES_COL, new VectorUDT, false)))
-    val dataset = sqlContext.createDataFrame(rowRDD, schema)
-
-    // Trains a LDA model
-    val lda = new LDA()
-      .setK(10)
-      .setMaxIter(10)
-      .setFeaturesCol(FEATURES_COL)
-    val model = lda.fit(dataset)
-    val transformed = model.transform(dataset)
-
-    val ll = model.logLikelihood(dataset)
-    val lp = model.logPerplexity(dataset)
-
-    // describeTopics
-    val topics = model.describeTopics(3)
-
-    // Shows the result
-    topics.show(false)
-    transformed.show(false)
-
-    // $example off$
-    sc.stop()
-  }
-}
-// scalastyle:on println